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Monday 6/7/21 08:41am
Posted By Hsin-I Hsu
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Training ML Models at the Edge with Federated Learning

Centralized machine learning (ML) is the ML workflow that most of us are familiar with today, where training is allocated to powerful servers which update model parameters using large datasets. The trained model may then be deployed to edge devices over the cloud for inference at the edge, or edge devices may collect and send data to the server for inference and receive back the server’s prediction(s).


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